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The most recent concern of all people on Earth is the increase in theconcentration of greenhouse gas in the atmosphere. The concentration of thesegases has risen rapidly over the last century and if the trend continues it cancause many adverse climatic changes. There have been ways implemented to curbthis by the government by limiting processes that emit a higher amount of CO2,one such greenhouse gas. However, there is mounting evidence that the CO2numbers supplied by the government do not accurately reflect the performance ofautomobiles on the road. Our proposal of using artificial intelligencetechniques to improve a previously rudimentary process takes a radical tack,but it fits the bill given the situation. To determine which algorithms andmodels produce the greatest outcomes, we compared them all and explored a novelmethod of ensembling them. Further, this can be used to foretell the rise inglobal temperature and to ground crucial policy decisions like the adoption ofelectric vehicles. To estimate emissions from vehicles, we used machinelearning, deep learning, and ensemble learning on a massive dataset.

A Comparative Study of Machine Learning and Deep Learning Techniques for Prediction of Co2 Emission in Cars